Finance teams are often blamed for slow approvals, delayed reports, missing receipts and month-end pressure. But many finance problems are not caused by the people in finance.
They are caused by systems.
A finance team may be working across accounting software, bank portals, expense tools, ERP, spreadsheets, emails, PDFs, shared drives and reporting dashboards. If those tools do not exchange data reliably, finance becomes the human integration layer.
That is when finance work becomes a software problem.
Manual work is a signal
Pleo’s 2025 treasury research reported that businesses across Europe estimate they spend 26.4 hours per week on manual treasury tasks. Pleo also highlights common sources of manual finance work such as cash position monitoring, spreadsheet maintenance and working across multiple systems.
The exact number will vary by company, but the pattern is familiar: finance teams spend too much time assembling, checking and moving information instead of using it.
Manual work is often a signal that the operating model has outgrown the software setup.
Common finance workflow problems
Receipts and attachments live everywhere
Receipts may arrive through card tools, email, mobile uploads, scanned PDFs, inboxes and messaging apps. Finance then has to match the receipt to the transaction, check policy, chase missing information and resolve exceptions.
This is not only a receipt problem. It is an information-flow problem.
Approvals are unclear
Approval rules often begin simply and then become complicated: department, amount, project, customer, country, vendor, budget owner, exception type. If those rules live in email threads or informal knowledge, the workflow slows down.
A useful approval flow should make the next step clear, show who owns it and keep an audit trail.
Data is entered twice
Duplicate data entry is one of the clearest signs that systems are not connected. The same invoice, vendor, employee, cost centre or project code may move through several tools by copy and paste.
That creates delay and increases the risk of errors.
Month-end becomes a rescue operation
When daily data flow is weak, month-end becomes a pressure point. Reconciliation, exception handling and reporting accumulate because the system does not produce enough reliable information during the month.
The problem appears at month-end, but it is usually created earlier in the workflow.
Planning uses outdated data
Finance leaders need current information to support decisions. If cash flow, spend, commitments or budget status depends on manual exports, the business may be making decisions on yesterday’s picture.
The same questions repeat
Employees repeatedly ask which policy applies, whether something needs approval, where to submit a document, whether a payment has gone through or how to classify an expense.
Repeated questions are often a documentation and access problem. The information may exist, but not where people work.
Start with the workflow, not another tool
The solution is not always to buy a new finance platform. Many companies already have several tools. The problem is that they do not work together well enough.
A practical improvement starts with workflow mapping:
- What triggers the work?
- Which systems are involved?
- Which data is copied manually?
- Which approvals are required?
- Which exceptions happen repeatedly?
- Where is sensitive information handled?
- Which reports are assembled manually?
- What does finance need to see earlier?
This mapping often reveals a small number of high-impact improvements.
Candidate improvements
Expense and invoice integrations
Connecting expense, invoice, accounting and ERP tools can reduce duplicate data entry and improve traceability. The goal is not to automate everything. It is to make routine data movement reliable and leave humans to handle exceptions.
Approval workflow redesign
A clear approval workflow can route requests based on amount, department, project, vendor or risk. It should also preserve human judgement for exceptions and high-impact decisions.
Reporting pipelines
Instead of manually exporting data from several systems, companies can build reporting pipelines that collect, transform and present information consistently. This improves the foundation for dashboards, forecasting and decision support.
Document classification
AI can help classify invoices, receipts, contracts, purchase orders and supporting documents. But AI should normally prepare or suggest classification rather than silently changing financial records without review.
Internal policy assistant
A controlled internal assistant can help employees find finance policies, approval rules, reimbursement guidance and submission instructions. Access control matters because finance information can be sensitive.
Exception handling
The best automation does not pretend exceptions disappear. It identifies them clearly, routes them to the right person and preserves context.
When normal automation is enough
Not every finance problem needs AI.
If the task is predictable and rule-based, normal software automation may be better: syncing data, validating fields, sending reminders, routing approvals, generating reports or checking required attachments.
AI becomes useful where the work involves unstructured documents, natural language, classification, summarisation, matching, triage or internal knowledge retrieval.
The strongest finance workflows often combine both: reliable integrations for structured data, AI assistance for unstructured information and human review for decisions.
Privacy, permissions and auditability
Finance data is sensitive. Any automation or AI-assisted workflow should consider:
- who can read finance data;
- who can approve changes;
- which data AI tools may process;
- how outputs are reviewed;
- which actions are logged;
- how errors are corrected;
- how access is removed when roles change.
Efficiency should not come at the cost of control.
The practical takeaway
Finance inefficiency is often a software and integration problem. When teams chase receipts, copy data, reconcile manually and answer repeated questions, the business should look at the workflow and the systems behind it.
The goal is not automation for its own sake. The goal is clearer information, fewer manual workarounds, better control and more time for finance to support business decisions.
Sources and inspiration
- Computerworld — Du klatter kostbare ressourcer væk på ineffektive arbejdsgange og for dårlig brug af digitale værktøjer: https://www.computerworld.dk/art/295612/du-klatter-kostbare-ressourcer-vaek-paa-ineffektive-arbejdsgange-og-for-daarlig-brug-af-digitale-vaerktoejer
- Computerworld — Regeringen jubler over AI-guld: Men ude i virkeligheden kæmper virksomhederne stadig med det samme gamle rod: https://www.computerworld.dk/art/295500/regeringen-jubler-over-ai-guld-men-ude-i-virkeligheden-kaemper-virksomhederne-stadig-med-det-samme-gamle-rod
- Computerworld — Danske virksomheder har tre år til at vinde kampen: https://www.computerworld.dk/art/295614/danske-virksomheder-har-tre-aar-til-at-vinde-kampen
- Pleo — How modern finance teams are reclaiming 26 hours per week: https://blog.pleo.io/en/how-finance-teams-save-26-hours-weekly
- Pleo — Buried Treasury: Uncovering the Secret to Financial Stability: https://www.pleo.io/en/treasury-report-2025
- Pleo — Why only 1 in 4 finance leaders have a clear sight of their cash flow: https://blog.pleo.io/en/why-only-1-in-4-finance-leaders-have-a-clear-sight-of-their-cash-flow
- Eurostat — 20% of EU enterprises use AI technologies: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2
- OWASP — Top 10 for LLM Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/